{"title":"A CORPUS-BASED STUDY OF LEXICAL COLLOCATIONS OF KEYWORDS FOUND IN ONLINE BUSINESS NEWS ARTICLES","authors":"Krittat Sukman, Wanpiya Triwatwaranon, Tiwphai Munkongdee, Narawan Chumnumnawin","doi":"10.46827/ejel.v7i3.4275","DOIUrl":null,"url":null,"abstract":"Business news articles are crucial for Business English students since they provide a rich source for learning vocabulary in their field of study and professional communication. This study investigates lexico-grammatical items specific to a specialised corpus of online business news reports. Its overall aim is twofold: 1) to generate a keywords list extracted from the self-built corpus; and 2) to identify lexical collocations of the first ten keywords. Seven hundred online business news articles from BBC news.com were compiled to build a corpus of business news. The data were analysed by the application of Antconc 3.5.9 developed by Anthony (2020). The results derived from the present study through a corpus-based analysis uncovered a list of keywords which were frequently used in online business news where the majority of the keywords were nouns (63.51%), verbs/nouns (8%), adjectives (7%), adjectives/nouns (4%), adverbs (2%), and verb (1%). From the selected top one hundred keywords, the lexical collocations of each keyword were identified with 3 combination types based on the set framework adapted from Benson et al.’s (2010). The obtained combination types include Adjective+Noun (40%), Noun+Noun (35%), and Noun+Verb (20%), respectively. The pedagogical implications and recommendations for future research were also discussed. Article visualizations:","PeriodicalId":226132,"journal":{"name":"European Journal of English Language Teaching","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of English Language Teaching","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46827/ejel.v7i3.4275","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Business news articles are crucial for Business English students since they provide a rich source for learning vocabulary in their field of study and professional communication. This study investigates lexico-grammatical items specific to a specialised corpus of online business news reports. Its overall aim is twofold: 1) to generate a keywords list extracted from the self-built corpus; and 2) to identify lexical collocations of the first ten keywords. Seven hundred online business news articles from BBC news.com were compiled to build a corpus of business news. The data were analysed by the application of Antconc 3.5.9 developed by Anthony (2020). The results derived from the present study through a corpus-based analysis uncovered a list of keywords which were frequently used in online business news where the majority of the keywords were nouns (63.51%), verbs/nouns (8%), adjectives (7%), adjectives/nouns (4%), adverbs (2%), and verb (1%). From the selected top one hundred keywords, the lexical collocations of each keyword were identified with 3 combination types based on the set framework adapted from Benson et al.’s (2010). The obtained combination types include Adjective+Noun (40%), Noun+Noun (35%), and Noun+Verb (20%), respectively. The pedagogical implications and recommendations for future research were also discussed. Article visualizations:
商务新闻文章对商务英语专业的学生来说是至关重要的,因为它们提供了丰富的学习词汇和专业交流的来源。这项研究调查了一个专门的在线商业新闻报道语料库的词汇语法项目。它的总体目标有两个:1)从自建语料库中提取关键字列表;2)识别前十个关键词的词汇搭配。来自BBC news.com的700篇在线商业新闻文章被编译成一个商业新闻语料库。采用Anthony(2020)开发的Antconc 3.5.9软件对数据进行分析。本研究通过基于语料库的分析得出了在线商业新闻中经常使用的关键词列表,其中大多数关键词是名词(63.51%)、动词/名词(8%)、形容词(7%)、形容词/名词(4%)、副词(2%)和动词(1%)。从选取的前100个关键词中,基于Benson et al.(2010)的集合框架,将每个关键词的词汇搭配识别为3种组合类型。所得到的组合类型分别为形容词+名词(40%)、名词+名词(35%)和名词+动词(20%)。本文亦讨论了教学意义及对未来研究的建议。可视化条